Information Recovery In Behavioral Networks

TL;DR

This paper develops entropy-based methods to recover behavioral choice information from network data, enabling analytical estimation of unknown parameters without extensive sampling.

Contribution

It introduces an entropy-driven approach using Cressie-Read functionals to analytically estimate behavioral flows in networks, expanding beyond traditional estimators.

Findings

Shannon and likelihood functionals effectively recover behavioral parameters.

The methods accurately reproduce observed data trends.

Analytical solutions outperform sampling-based approaches.

Abstract

In the context of agent based modeling and network theory, we focus on the problem of recovering behavior-related choice information from origin-destination type data, a topic also known under the name of network tomography. As a basis for predicting agents' choices we emphasize the connection between adaptive intelligent behavior, causal entropy maximization and self-organized behavior in an open dynamic system. We cast this problem in the form of binary and weighted networks and suggest information theoretic entropy-driven methods to recover estimates of the unknown behavioral flow parameters. Our objective is to recover the unknown behavioral values across the ensemble analytically, without explicitly sampling the configuration space. In order to do so, we consider the Cressie-Read family of entropic functionals, enlarging the set of estimators commonly employed to make optimal use of the available information. More specifically, we explicitly work out two cases of particular interest: Shannon functional and the likelihood functional. We then employ them for the analysis of both univariate and bivariate data sets, comparing their accuracy in reproducing the observed trends.